Grab lab file using command line:

# Step 1
cd ~/Documents
mkdir lab11
cd lab11

# Step 2
wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.Rmd

And remember to set eval=TRUE

Learning Goals

Read in and process the COVID dataset from the New York Times GitHub repository
Create interactive graphs of different types using plot_ly() and ggplotly() functions
Customize the hoverinfo and other plot features
Create a Choropleth map using plot_geo()

Lab Description

We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.

The objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv”
Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv””
Merge datasets

## data extracted from New York Times state-level data from NYT Github repository
# https://github.com/nytimes/covid-19-data

## state-level population information from us_census_data available on GitHub repository:
# https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data

### FINISH THE CODE HERE ###
# load COVID state-level data from NYT
cv_states <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))

### FINISH THE CODE HERE ###
# load state population data
state_pops <- as.data.frame(data.table::fread("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))
state_pops$abb <- state_pops$state
state_pops$state <- state_pops$state_name
state_pops$state_name <- NULL

### FINISH THE CODE HERE
cv_states <- merge(cv_states, state_pops, by="state")

2. Look at the data

Inspect the dimensions, head, and tail of the data
Inspect the structure of each variables. Are they in the correct format?

dim(cv_states)

## [1] 51126     9

head(cv_states)

##     state       date fips   cases deaths geo_id population pop_density abb
## 1 Alabama 2022-03-17    1 1290692  18998      1    4887871    96.50939  AL
## 2 Alabama 2022-02-24    1 1276580  18102      1    4887871    96.50939  AL
## 3 Alabama 2021-06-10    1  547135  11252      1    4887871    96.50939  AL
## 4 Alabama 2022-01-28    1 1153149  16826      1    4887871    96.50939  AL
## 5 Alabama 2021-06-08    1  546540  11220      1    4887871    96.50939  AL
## 6 Alabama 2020-11-26    1  241957   3572      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips  cases deaths geo_id population pop_density abb
## 51121 Wyoming 2021-10-11   56  95137   1041     56     577737    5.950611  WY
## 51122 Wyoming 2022-06-11   56 159707   1824     56     577737    5.950611  WY
## 51123 Wyoming 2021-06-17   56  61425    734     56     577737    5.950611  WY
## 51124 Wyoming 2021-05-25   56  59870    719     56     577737    5.950611  WY
## 51125 Wyoming 2021-12-05   56 111812   1428     56     577737    5.950611  WY
## 51126 Wyoming 2021-08-15   56  68272    793     56     577737    5.950611  WY

str(cv_states)

## 'data.frame':    51126 obs. of  9 variables:
##  $ state      : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ date       : IDate, format: "2022-03-17" "2022-02-24" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  1290692 1276580 547135 1153149 546540 241957 89349 1531305 194892 67011 ...
##  $ deaths     : int  18998 18102 11252 16826 11220 3572 1603 20533 2973 1287 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : chr  "AL" "AL" "AL" "AL" ...

3. Format the data

Make date into a date variable
Make state into a factor variable
Order the data first by state, second by date
Confirm the variables are now correctly formatted
Inspect the range values for each variable. What is the date range? The range of cases and deaths?

# format the date
cv_states$date <- as.Date(cv_states$date, format="%Y-%m-%d")

# format the state and state abbreviation (abb) variables
state_list <- unique(cv_states$state)
cv_states$state <- factor(cv_states$state, levels = state_list)
abb_list <- unique(cv_states$abb)
cv_states$abb <- factor(cv_states$abb, levels = abb_list)

### FINISH THE CODE HERE 
# order the data first by state, second by date
cv_states = cv_states[order(cv_states$state, cv_states$date),]

# Confirm the variables are now correctly formatted
str(cv_states)

## 'data.frame':    51126 obs. of  9 variables:
##  $ state      : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date       : Date, format: "2020-03-13" "2020-03-14" ...
##  $ fips       : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ cases      : int  6 12 23 29 39 51 78 106 131 157 ...
##  $ deaths     : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ geo_id     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ population : int  4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ...
##  $ pop_density: num  96.5 96.5 96.5 96.5 96.5 ...
##  $ abb        : Factor w/ 52 levels "AL","AK","AZ",..: 1 1 1 1 1 1 1 1 1 1 ...

head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 820 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 625 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 881 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 366 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 663 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 472 Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

tail(cv_states)

##         state       date fips  cases deaths geo_id population pop_density abb
## 50845 Wyoming 2022-11-04   56 178866   1914     56     577737    5.950611  WY
## 50412 Wyoming 2022-11-05   56 178866   1914     56     577737    5.950611  WY
## 50833 Wyoming 2022-11-06   56 178866   1914     56     577737    5.950611  WY
## 50527 Wyoming 2022-11-07   56 178866   1914     56     577737    5.950611  WY
## 51116 Wyoming 2022-11-08   56 179366   1917     56     577737    5.950611  WY
## 51087 Wyoming 2022-11-09   56 179366   1917     56     577737    5.950611  WY

# Inspect the range values for each variable. What is the date range? The range of cases and deaths?
head(cv_states)

##       state       date fips cases deaths geo_id population pop_density abb
## 820 Alabama 2020-03-13    1     6      0      1    4887871    96.50939  AL
## 625 Alabama 2020-03-14    1    12      0      1    4887871    96.50939  AL
## 881 Alabama 2020-03-15    1    23      0      1    4887871    96.50939  AL
## 366 Alabama 2020-03-16    1    29      0      1    4887871    96.50939  AL
## 663 Alabama 2020-03-17    1    39      0      1    4887871    96.50939  AL
## 472 Alabama 2020-03-18    1    51      0      1    4887871    96.50939  AL

summary(cv_states)

##            state            date                 fips           cases         
##  Washington   : 1024   Min.   :2020-01-21   Min.   : 1.00   Min.   :       1  
##  Illinois     : 1021   1st Qu.:2020-11-02   1st Qu.:16.00   1st Qu.:   88810  
##  California   : 1020   Median :2021-07-06   Median :29.00   Median :  341686  
##  Arizona      : 1019   Mean   :2021-07-05   Mean   :29.78   Mean   :  812490  
##  Massachusetts: 1013   3rd Qu.:2022-03-09   3rd Qu.:44.00   3rd Qu.:  956016  
##  Wisconsin    : 1009   Max.   :2022-11-09   Max.   :72.00   Max.   :11392945  
##  (Other)      :45020                                                          
##      deaths          geo_id        population        pop_density       
##  Min.   :    0   Min.   : 1.00   Min.   :  577737   Min.   :    1.292  
##  1st Qu.: 1365   1st Qu.:16.00   1st Qu.: 1805832   1st Qu.:   43.659  
##  Median : 5110   Median :29.00   Median : 4468402   Median :  107.860  
##  Mean   :11362   Mean   :29.78   Mean   : 6404076   Mean   :  422.943  
##  3rd Qu.:14380   3rd Qu.:44.00   3rd Qu.: 7535591   3rd Qu.:  229.511  
##  Max.   :97077   Max.   :72.00   Max.   :39557045   Max.   :11490.120  
##                                                     NA's   :972        
##       abb       
##  WA     : 1024  
##  IL     : 1021  
##  CA     : 1020  
##  AZ     : 1019  
##  MA     : 1013  
##  WI     : 1009  
##  (Other):45020

min(cv_states$date)

## [1] "2020-01-21"

max(cv_states$date)

## [1] "2022-11-09"

4. Add `new_cases` and `new_deaths` and correct outliers

Add variables for new cases, new_cases, and new deaths, new_deaths:
- Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2
Filter to dates after June 1, 2022
Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?
Correct outliers: Set negative values for new_cases or new_deaths to 0
Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths
Get the rolling average of new cases and new deaths to smooth over time
Inspect data again interactively

# Add variables for new_cases and new_deaths:
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  cv_subset = cv_subset[order(cv_subset$date),]
  # add starting level for new cases and deaths
  cv_subset$new_cases = cv_subset$cases[1]
  cv_subset$new_deaths = cv_subset$deaths[1]
  #### FINISH THE CODE HERE ###
  for (j in 2:nrow(cv_subset)) {
    cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] 
    cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases
  cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths
}
cv_states <- cv_states %>% dplyr::filter(date >= "2022-06-01")

### FINISH THE CODE HERE ###
p1<-ggplot(cv_states, 
           aes( x=date, y=new_cases, color=state  )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p1)

p1<-NULL # to clear from workspace
### FINISH THE CODE HERE ###
p2<-ggplot(cv_states, 
           aes(x=date, y=new_deaths, color=state )
           ) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

p2<-NULL # to clear from workspace
# set negative new case or death counts to 0
cv_states$new_cases[cv_states$new_cases<0] = 0
cv_states$new_deaths[cv_states$new_deaths<0] = 0
# Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths`
for (i in 1:length(state_list)) {
  cv_subset = subset(cv_states, state == state_list[i])
  # add starting level for new cases and deaths
  cv_subset$cases = cv_subset$cases[1]
  cv_subset$deaths = cv_subset$deaths[1]
  for (j in 2:nrow(cv_subset)) {
    cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1]
    cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1]
  }
  # include in main dataset
  cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases
  cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths
}

# Smooth new counts
cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>% round(digits = 0)
cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>% round(digits = 0)

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

#p2=NULL

# Inspect data again interactively
p2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5)
ggplotly(p2)

#p2=NULL

5. Add additional variables

Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:
- per100k = cases per 100,000 population
- newper100k= new cases per 100,000
- deathsper100k = deaths per 100,000
- newdeathsper100k = new deaths per 100,000
Add a “naive CFR” variable representing deaths / cases on each date for each state
Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture

### FINISH CODE HERE
# add population normalized (by 100,000) counts for each variable
cv_states$per100k =  as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))
cv_states$newper100k =  as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
cv_states$deathsper100k =  as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1))
cv_states$newdeathsper100k =  as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1))

# add a naive_CFR variable = deaths / cases
cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))

# create a `cv_states_today` variable
cv_states_today = subset(cv_states, date==max(cv_states$date))

II. Interactive plots

6. Explore scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)
- Use hover to identify any outliers.
- Remove those outliers and replot.
Choose one plot. For this plot:
- Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes
- Add layout information to title the chart and the axes
- Enable hovermode = "compare"

# pop_density vs. cases
### FINISH THE CODE HERE ###
cv_states_today %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# filter out "District of Columbia"
cv_states_today_scatter <- cv_states_today %>% filter(state!="District of Columbia")
# pop_density vs. cases after filtering
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~cases, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# pop_density vs. deathsper100k
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~newdeathsper100k, 
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))

# Adding hoverinfo
cv_states_today_scatter %>% 
  plot_ly(x = ~pop_density, y = ~deathsper100k,
          type = 'scatter', mode = 'markers', color = ~state,
          size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5),
          hoverinfo = 'text',
          text = ~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ",
                        deathsper100k, sep=""), sep = "<br>")) %>%
  layout(title = "Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",
                  yaxis = list(title = "Deaths per 100k"), xaxis = list(title = "Population Density"),
         hovermode = "compare")

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`

For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()
- What’s the geom_*() we need here?
Explore the pattern between \(x\) and \(y\) using geom_smooth()
- Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?

### FINISH THE CODE HERE ###
p <- ggplot(cv_states_today_scatter, aes(x=pop_density, y=per100k, color=state, size=population)) + geom_point() + geom_smooth() 
ggplotly(p)

### For specified date

pick.date = "2022-10-15"

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states %>% filter(date==pick.date) %>% select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Make sure both maps are on the same color scale
shadeLimit <- 35

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", pick.date), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_pick.date <- fig

#############
### Map for today's date

# Extract the data for each state by its abbreviation
cv_per100 <- cv_states_today %>%  select(state, abb, newper100k, cases, deaths) # select data
cv_per100$state_name <- cv_per100$state
cv_per100$state <- cv_per100$abb
cv_per100$abb <- NULL

# Create hover text
cv_per100$hover <- with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

# Create the map
fig <- plot_geo(cv_per100, locationmode = 'USA-states') %>% 
  add_trace(
    z = ~newper100k, text = ~hover, locations = ~state,
    color = ~newper100k, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = paste0("Cases per 100k: ", Sys.Date()), limits = c(0,shadeLimit))
fig <- fig %>% layout(
    title = paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),
    geo = set_map_details
  )
fig_Today <- fig


### Plot together 
subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)

FINISH CODE HERE

Map state, date, and new_cases to a matrix

library(tidyr) cv_states_mat <- cv_states %>% select(state, date, new_cases) %>% dplyr::filter(date>as.Date(“2021-06-15”)) cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) <- cv_states_mat2\(date cv_states_mat2\)date <- NULL cv_states_mat2 <- as.matrix(cv_states_mat2)

Create a heatmap using plot_ly()

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=“heatmap”, showscale=T)

Repeat with newper100k

cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% dplyr::filter(date>as.Date(“2021-06-15”)) cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) <- cv_states_mat2\(date cv_states_mat2\)date <- NULL cv_states_mat2 <- as.matrix(cv_states_mat2)

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=“heatmap”, showscale=T)

Create a second heatmap after filtering to only include dates every other week

filter_dates <- seq(as.Date(“2021-06-15”), as.Date(“2021-11-01”), by=________)

cv_states_mat <- cv_states %>% select(state, date, newper100k) %>% filter(date %in% filter_dates) cv_states_mat2 <- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) <- cv_states_mat2\(date cv_states_mat2\)date <- NULL cv_states_mat2 <- as.matrix(cv_states_mat2)

Create a heatmap using plot_ly()

plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=“heatmap”, showscale=T)

Lab 11 - Interactive Visualization

11/09/2022

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add `new_cases` and `new_deaths` and correct outliers

5. Add additional variables

II. Interactive plots

6. Explore scatterplots using `plot_ly()`

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`

FINISH CODE HERE

Map state, date, and new_cases to a matrix

Create a heatmap using plot_ly()

Repeat with newper100k

Create a second heatmap after filtering to only include dates every other week

Create a heatmap using plot_ly()

Lab 11 - Interactive Visualization

11/09/2022

Learning Goals

Lab Description

Steps

I. Reading and processing the New York Times (NYT) state-level COVID-19 data

1. Read in the data

2. Look at the data

3. Format the data

4. Add new_cases and new_deaths and correct outliers

5. Add additional variables

II. Interactive plots

6. Explore scatterplots using plot_ly()

7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()

FINISH CODE HERE

Map state, date, and new_cases to a matrix

Create a heatmap using plot_ly()

Repeat with newper100k

Create a second heatmap after filtering to only include dates every other week

Create a heatmap using plot_ly()

4. Add `new_cases` and `new_deaths` and correct outliers

6. Explore scatterplots using `plot_ly()`

7. Explore scatterplot trend interactively using `ggplotly()` and `geom_smooth()`